Research on multi-trait genome association study method based on Shannon information entropy.
BACKGROUND: Genetic analysis of complex traits is crucial for elucidating disease mechanisms and biological inheritance processes. However, traditional Genome-wide Association Study (GWAS) for single trait often fail to capture the synergistic effects of genetic loci on multiple traits. METHODS: This study proposes a method for analyzing the association between multiple traits and gene regions based on Shannon information entropy. Innovatively, Shannon information entropy is introduced to integrate gene region information as genetic entropy, thereby constructing an Inverse Shannon Entropy-Multi-Trait Association Analysis of Gene Region genetic model (InvSE-MTAGR). Furthermore, a partial regression test is applied to the model to establish the Inverse Partial Shannon Entropy-Multi-Trait Association Analysis of Gene Region method (InvPSE-MTAGR). When performing multi-trait analysis with InvSE-MTAGR, the method achieved statistical significance by accumulating minor effects, thereby enhancing the ability to identify pleiotropic gene regions. RESULTS: The simulation results showed that the proposed multi-trait gene region association analysis method performed well in terms of both Type I error rate control and statistical power. Leveraging tomato and sorghum datasets for validation, the proposed multi-trait gene region association analysis method based on Shannon information entropy accurately pinpointed most of the gene regions harboring candidate genes. CONCLUSION: The study reveals the advantage of multi-trait method in integrating weak-effect pleiotropic signals and capturing the correlation among traits, which provides an efficient theoretical tool for dynamic analysis of complex multi-trait genetic networks and multi-target collaborative breeding of crops.